# -*- coding: utf-8 -*-
from datetime import timedelta
from jms.dim.dim_yl_oms_order_interceptor_base_dt import jms_dim__dim_yl_oms_order_interceptor_base_dt
from jms.dim.dim_network_whole_massage import jms_dim__dim_network_whole_massage
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator

jms_dm__dm_customer_complaint_order_interceptor_dt = SparkSqlOperator(
    task_id='jms_dm__dm_customer_complaint_order_interceptor_dt',
    email='shenjiaming@jtexpress.com',
    pool_slots=2,
    master='yarn',
    name='jms_dm__dm_customer_complaint_order_interceptor_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_customer_complaint_order_interceptor_dt/execute.hql',
    driver_memory='5G',
    executor_memory='8G',
    executor_cores=4,
    num_executors=10,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    yarn_queue='pro',
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          #'spark.dynamicAllocation.maxExecutors': 30,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.maxExecutors': 27,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '2G',  # 堆外内存
          },
    #hiveconf={'hive.exec.dynamic.partition': 'true',
#              'hive.exec.dynamic.partition.mode': 'nonstrict',
#              'hive.exec.max.dynamic.partitions.pernode': 5000,
#              'hive.exec.max.dynamic.partitions': 30000
#              },
    execution_timeout=timedelta(minutes=30),
)

jms_dm__dm_customer_complaint_order_interceptor_dt << [jms_dim__dim_yl_oms_order_interceptor_base_dt
                                                       ,jms_dim__dim_network_whole_massage
                                                       ]
